Storage Volume Compression Ranking for Cost-Aware Scheduling
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Solution Overview
Problem
Current storage technologies lack an efficient method to prioritize and schedule data compression across multiple storage volumes, leading to suboptimal storage space savings and cost savings, as existing methods do not effectively predict and rank potential compression benefits across different volumes.
Innovation Solution
A method that examines information from multiple storage volumes to predict storage space savings and compression cost savings, ranking volumes based on these predictions and scheduling compression accordingly, using a system that includes a manager system to perform terabyte storage volume savings and cost savings predictions, and scheduling data compression based on these rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is performed on all storage volumes without prioritization, then storage space savings may be achieved, but computational resources are wasted on volumes that provide minimal compression benefits
Solution Approach 1:
The system performs preliminary analysis of storage volumes to predict compression space savings before actually executing compression. By examining volume characteristics and calculating predicted savings in advance, the system identifies which volumes are most likely to benefit from compression, thereby avoiding wasteful compression of volumes that would provide minimal benefits.
Solution Approach 2:
The system changes the parameter of compression priority by ranking storage volumes based on predicted space savings. Instead of treating all volumes equally, the system dynamically adjusts compression scheduling based on calculated parameters such as predicted savings, volume size, and compression cost, optimizing resource allocation to high-value targets.
2Productivity
If compression is scheduled without ranking volumes by predicted savings, then compression processes can be distributed evenly, but overall storage efficiency optimization is reduced
Solution Approach 1:
The system implements feedback by continuously monitoring actual compression results and comparing them with predicted savings. This feedback loop allows the system to refine its prediction algorithms and ranking mechanisms over time, improving storage efficiency optimization while managing scheduling complexity through learned patterns rather than static rules.
Solution Approach 2:
The system segments the compression scheduling process into distinct phases: prediction phase (analyzing volume characteristics), ranking phase (ordering volumes by predicted savings), and execution phase (performing compression in ranked order). This segmentation makes the complex optimization problem more manageable and allows each phase to be optimized independently.
3Productivity
If compression is performed on volumes with low predicted savings, then more volumes can be compressed, but the return on computational investment decreases
Solution Approach 1:
The system dynamically changes the parameter of compression selection by adjusting the threshold for minimum predicted savings. Instead of compressing all volumes or using a fixed threshold, the system adapts the selection criteria based on available computational resources, current storage needs, and predicted compression benefits, optimizing the ratio of volumes compressed to computational cost incurred.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining information of first through Nth storage volumes and based on the examining providing for each storage volume of the first through Nth storage volumes a predicted storage space savings value, the predicted storage space savings value indicating a predicted terabyte volume of storage space savings producible by performance of data compression of data stored on the storage volume; predicting a per terabyte compression cost savings associated with compressing one or more storage volume of the first through Nth storage volumes, and providing a ranking of storage volumes of the first through Nth storage volumes based on the examining and the predicting; and scheduling a compression of storage volumes of the first through Nth storage volumes based on the ranking of storage volumes of the first through Nth storage volumes.